Marine oil spills have caused serious harm to the costal ecological environment and marine economy. Synthetic aperture radar (SAR) has become a major equipment for oil spill detection because of its advantages of all-day and all-weather observation capability. In this paper, the complex convolutional neural network (CVCNN) framework is applied for marine oil spills classification. The classification performance of different polarization modes on marine oil spills classification is analyzed. Experimental results show that CP SAR modes have comparable performance as QP mode for marine oil spills classification. Among them, the Circular Transmit and Linear Receive (CTLR) mode has the best classification performance in the framework of CVCNN while DP (VVVH) mode is another promising alternative with much simpler hardware configuration requirement.

Brightness temperature (TB) derived from the Chang’e-2 microwave radiometer (MRM) data has provided a useful way to study the thermal and dielectric properties of the subsurface deposits on the Moon. However, the obvious TB change with the latitude, named latitude effect, has highly limited the application of the MRM data. To solve this problem, a new TB mapping method, named normalized TB (nTB) mapping method, is developed, which is defined as the ratio between the TB and the standard TB at the same point. Based on the newly derived global nTB maps, we identified and classified four types of subsurface deposits with distinct dielectric properties, two of which indicate the abnormally high heat flux or the existence of the granitic systems and the existence of the surface rocks, respectively. Moreover, the nTB at the daytime demonstrates a strong correlation with both the TiO2 and FeO abundances of subsurface deposits, the latter of which has been severely underestimated by the previous studies directly using MRM data. This work is significant to improve the understanding of the basaltic volcanism and thermal evolution of the Moon.

Sinus Medii has various geological features, and its location in the central part of the lunar nearside is ideal for calibrating ground-based lunar observation instruments. In this work, the Chang’E-2 (CE-2) microwave radiometer (MRM) data were used to evaluate the microwave thermal properties of the surface deposits in Sinus Medii combined with the previous geological results based on optical data. The findings are as follows: (1) a lunar calibration site (4.98°W, 3.02°N) is found where the TB performances show a good agreement with the compositions of the surface deposits, (2) a TB anomaly (0.24°W, 0.76°N) was found which may be related to high rock abundance in a crater, and (3) a possible cryptomare is revealed in the light plain southeast of Sinus Medii. This study of the microwave thermal properties in Sinus Medii helps search for calibration sites for ground-based observation instruments.

The brightness temperature (TB) features extracted from Chang’e Lunar Microwave Sounder (CELMS) data have been proved their superiority to study mare basalt. In this paper, dimension reduction and feature space analysis are conducted on TBfeatures to fully understand the data distribution and reduce the feature redundancy in the classification process based on two methods - Principal Component Analysis (PCA) and Nonnegative Matrix Factorization (NNMF). The results showed that PCA and NNMF can effectively enhance the classification capability of early(?)- and late(?)-age mare basalt respectively, and proved the necessity of dimension reduction for CELMS TBfeatures due to the largely-existing redundancy.

The Chang’e-1/2 satellites carried microwave radiometers (MRM), which supplied unique passive microwave data, supplementing visible, thermal infrared, and radar data in current lunar studies. However, the application of MRM data is constrained by the limited amount of the original dataset, which cannot express spatial variations at given local times. In this study, we present a novel approach to constructing a local-time brightness temperature (TB) model using barycentric interpolation based on Delaunay tetrahedralization. This model enables the generation of a complete TB dataset, producing TB maps that are both continuous and self-consistent in both spatial and temporal domains. Compared to the traditional methods, those generated in this work avoid a ~7-K bias on a global scale. Furthermore, the interpolated maps offer superior representations of the TB over time for various lunar surfaces. The preliminary evaluations on global and regional scales hint important application of MRM data in studying the geological features of the Moon.

The recent development of marine transportation and offshore oil exploration and exploitation has increased the risk of marine oil spill accidents. Oil pollution is one of the most complex marine pollutions, it will seriously threaten the marine ecological environment. Synthetic aperture radar (SAR) has been widely used in marine monitoring for its all-day and all-weather imaging capability. Polarimetric SAR can obtain polarimetric scattering information of the ground targets, which provides a new means for marine oil spill detection. Besides, with the recent development of machine learning algorithms, especially convolutional neural networks, higher oil spill classification accuracy can be obtained given more training datasets. However, currently most neural network models are applied to real-valued input and cannot fully exploit the phase information contained in complex-valued data of polarimetric SAR images. In this study, a classification approach of marine oil spills in polarimetric SAR images is presented based on the complex-valued convolutional neural network (CVCNN). The experimental results show that the proposed approach outperforms the real-valued convolutional neural network (RVCNN) in both the detection of oil spills from sea surface and the classification of crude oil films and biogenic oil films.

Studying Tsiolkovskiy crater with mare basalt in the floor helps to better understand the volcanic evolution and geological events on the lunar far side. In this work, the thermal behaviors of the surface materials in Tsiolkovskiy are evlated with the microwave radiometer data from Chang'e-2 satellite. The main results are as follows. (1) The thermal behaviors in the central peak coincide with those of rocks. (2) A small patch with higher substrate temperature is discovered in the southwestern part of crater floor with basaltic material. (3) The difference between the eastern and western half of Tsiolkovskiy crater is pointed out according to the brightness temperature behaviors.

Studying Korolev basin may provide meaningful information about the Megabasin on the lunar surface. In this study, the brightness temperature (TB) maps derived with Chang'e-2 microwave radiometer data are used to study the thermophysical features of the surface materials within and outside Korolev basin. Here, for the first time, a high TB anomaly in the highland crater is revealed with the TB data. Moreover, four surface units are indicated by the TB performances at day and night, which likely reflects the thermophysical features of the ejecta mainly from Orientale, Hertzsprung, and Apollo events. This study is essential to improve understanding the surface and thermal evolution of the highland craters.

The extraction of urban impervious surface information plays a key role in the studies of urbanization and its related environmental issues. Optical and SAR remote sensing provides complementary information to improve the accuracy of impervious mapping. However, the fusing of information acquired by different sensors is challenging. Optical and SAR features have distinct characteristics, and require different classification strategy and classification types. In this study, a strategy of fusing multi-spectral optical and polarimetric SAR data at decision-level is proposed. Features are extracted from optical and SAR data, then staked auto-encoder is applied to achieve the land use and land cover classification separately. D-S evidence theory is used to fuse the classification result and the imperious surface map is derived. The experiment was conducted in a highly complex urban area of Hong Kong and the results proves the soundness of the method.

In this paper, a unified framework was applied to derive compact polarimetric Synthetic Aperture Radar (SAR) features under general transmit and linear receive polarization conditions. Following the rationale of polarization signature, the characteristics of features derived by transmission of variant roll angles and ellipticity are analyzed. Statistical distance was proposed to quantitatively measure the performance of these compact polarimetric SAR features on marine oil spill classification. Experiment was conducted on a Radarsat-2 quad-pol SAR scene acquired during a controlled oil-on-water exercise.

This study proposed a novel method to analyze slick-free and oil covered sea surface backscattering in the special case of Deepwater Horizon (DWH) oil spill accident based on combination of tilted Bragg scattering and volume scattering components. The DWH accident represents a particular and challenging case due to the very large amount of leaked oil that came from the bottom of the ocean. The proposed scattering model consists of first estimate the large scale tilting angle of sea surface Bragg scattering mechanism and then of the retrieval, through its linear relationship with the relative dielectric constant, of oil-insea volume concentration. Finally, Bragg and volume scattering components can be estimated which provide useful information for a better understanding of i) the sea surface status and ii) the weak-damping properties the leaked oil due to purification/emulsification phenomena. The model is tested considering actual UAVSAR L-band fully-polarimetric SAR data.

Oil spill has been a crucial hazard to the coastal environment. A major difficulty of Synthetic Aperture Radar (SAR) based oil-spill detection algorithms is the classification between mineral oil and biogenic look-alikes. Polarimetric SAR features provides helpful information in disguising mineral oil and its look-alikes. In this study, we focused on the extraction and selection of fully polarimetric SAR features for the classification between mineral and biogenic look-alikes. Three mainly used supervised classifiers including Support vector machine (SVM), Artificial neural network (ANN) and Maximum likelihood classification (ML) were comparatively studied. In the experiment, classification performance increases with the growth of the feature number initially, but still fluctuates or decreases after the sufficient features are considered. It was also discovered that among all the classifiers, support vector machine performed best.

Polarimetric features retrieved from CTLR (circularly transmit and linearly receive) Synthetic Aperture Radar (SAR) data was analysed in details. A new parameter, namely, Damping Status Sensitivity Index (DSSI) was proposed for quantitatively evaluating the PolSAR characteristics' capability of discriminating different damping patterns of oil slicks and clean seawater. The L-band Uninhabited Aerial Vehicle SAR (UAVSAR) data was utilized in the experiments. It was shown that polarimetric characteristics retrieved from CTLR compact polarimetric SAR data were nearly as same as those derived from fully polarimetric SAR data and can be applied for discriminating different damping status of oil spill and look-likes.

In this paper a polarimetric SAR data study is undertaken over the critical and peculiar case of the Deepwater Horizon (DWH) oil spill accident occurred in the Gulf of Mexico in 2010. It is a complex case that still calls for deeper physical/chemical characterization due to its special nature. Such an oil spill accident is in fact related to a deep/ultra-deep sea drilling, that although of paramount interest in gas and oil exploitation, need special care and safety actions to manage high oil pressure and potential vast environmental oil spill long-term and short-term impact.

Synthetic Aperture Radar (SAR) data has been reported to be able to provide complementary information towards optical remote sensing data for improving the urban impervious surface estimation. However, most existing researches were focused on using only single polarization SAR data. This study presents a preliminary experiment on the combined use of multispectral optical data and dual polarization SAR data for impervious surfaces estimation. Experimental results using SPOT-5 and ALOS PALSAR images showed a consistent result compared with our previous result using single polarization SAR data. Comparison results showed that not every polarimetric feature was able to provide positive effect to the impervious surfaces estimation. Compared with using only optical and SAR data, the separated HH and HV polarization data provided a positive effect to the result by improving the accuracy. The incorporation of both Entropy and Alpha features were also able to improve the accuracy. However, the HH/HV ratio and the separated use of Entropy did not provide positive results. A combination of all the features turned out to obtain the highest accuracy compared with using a subset of the features.

An improved reconstruction algorithm is proposed for compact polarimetric (CP) synthetic aperture radar (SAR) on oil spill detection. Based on the differences in statistical behavior between open and oil-covered sea surfaces, the proposed algorithm can iteratively reconstruct quad-pol SAR images from CP SAR data. During the experiment, it outperformed two existing compact SAR reconstruction algorithms in terms of both statistical and information theoretical analysis.

Accurate land covers classification is challenging in urban areas due to the diversity of urban land covers. This study presents a classification strategy with combined optical and Synthetic Aperture Radar (SAR) images using Random Forest (RF). Optimization of RF is conducted, indicating the optimal number of decision trees is 10 and the optimal number of features is 4 for splitting each tree node. The overall accuracy (OA) and Kappa coefficient are used to assess the classification. Result shows that classification with combined optical and SAR images (OA: 69.08%; Kappa: 0.6288) is higher than that with single optical image (OA: 81.43%; Kappa: 0.7770). Benefits of the combined use of optical and SAR images mainly come from reducing the confusions between water and shade, and between bare soil and dark impervious surfaces.

Mechanical energy input into the ocean from the atmosphere is primarily produced via the ocean surface waves. First, the total wind generation surface wave energy is estimated as nearly 80 TW (1 TM = 1012W), based on an empirical formulation and the wave age (β) retrieved from the Earth Remote Sensing-1/2 satellite scatterometer observations. Second, the distribution of the wind-generated surface wave energy density shows that the main input occurs in the westerlies in the Southern Hemisphere and could reach up to 3.58 W/m2with an average value of 0.24 W/m2in the global ocean during a seven-day period. Third, we find that the downward energy flux rate from wind to surface waves can reach around 24% in the Southern Ocean and storm tracks in the northwest Pacific and Atlantic Oceans where the wind waves dominate, but a low downward rate of around 7% in tropical oceans with an average of 11% over the global oceans is observed.

IEEE, IEEE Geosci & Remote Sensing Soc, NASA, NOAA, USN Off Res, Japan Aerosp Explorat Agcy, Natl Polar orbiting Operat Environm Satellite Syst, Ball Aerosp & Technologies Corp, Int Union Radio Sci, Elect & Telecommun Res Inst, Korea Sci & Engn Fdn, Korea Natl Tourism Org, Korea Telecommun